Course 825:
MLOps on Google Cloud

(3 days)

 

Course Description

Machine Learning can be used to create predictive algorithms using your data and today’s extremely powerful computers. However, getting these ML algorithms from the research lab into production can be challenging. In this course, you will learn to use the power of Google Cloud and its suite of data analytics and ML tools to more easily process your data, train your models, deploy your models, and manage them in production.

Learning Objectives

  • Simplify and automate machine learning operations using Google Cloud tools
  • Learn the basics of Machine Learning
  • Implement the steps required to build a production ML pipeline
  • Build batch and streaming data pipelines with Google Cloud Dataflow
  • Perform Feature Engineering using Dataflow and BigQuery
  • Train and validate ML models using BigQuery ML, Auto ML, and TensorFlow
  • Deploy models using Google Cloud automation
  • Leverage Vertex AI to manage ML datasets, models, experiments, and deployment
  • Program Vertex AI pipelines using Kubeflow

Who Should Attend

Data Engineers, developers, and others who are working on machine learning projects and want to learn how to use Google Cloud tools to automate the training and deployment of their ML models.

Hands-On Activities

This course includes hands-on labs that reinforce your learning and provide a template for getting started on your real-world use cases.

Course Outline

1. Machine Learning

  • ML Basics
    • ML Examples
    • Models
    • Examples
    • Features 
    • Labels
    • Training, Validation Data, and Test Data
    • Training
    • Validation
    • Hyperparameters
    • Prediction
  • Linear Regression Models
    • Linear Regression
    • Linear Regression Examples
    • Validating Linear Regression Models
    • RMSE
    • Exercise: Building a Linear Regression Model 
  • Classification Models
    • Classification
    • Classification Examples
    • Validating Classification Models
    • Accuracy 
    • Precision
    • Recall
    • Area Under Receiver Operator Curve (AUROC)
    • Exercise: Building a Classification Model 
  • Neural Networks
    • Neural Networks
    • Example
    • Neurons
    • Layers
    • Weights
    • Exercise: Building a Neural Network 

2. MLOps 

  • ML Steps
    • Data Collection
    • Feature Engineering
    • Training
    • Validation
    • Putting Models Into Production
  • ML Pipelines
    • Batch ML Pipelines
    • Online Prediction Models
    • On-device Prediction Models
    • Streaming ML Pipelines

3. Creating Data Pipelines with Dataflow

  • Dataflow Basics
  • Apache Beam
    • Creating a Pipeline
    • Pipeline Basics
    • PCollections
    • Transforms
    • I/O
    • Dataflow Templates
    • Exercise: Programming Apache Beam Pipelines
  • Batch Dataflow Pipelines
    • Dataflow Basics
    • Running Dataflow Jobs
    • Exercise: Running Dataflow Jobs
  • Streaming Dataflow Pipelines
    • Example
    • Exercise: Building a Streaming Pipeline with DataFlow Templates 

 4. Feature Engineering

  • Feature Engineering Basics
    • Analyzing Features
    • Discovering Relationships in Data
    • Dealing with Incomplete or Invalid Data
    • One-Hot Encoding
  • Feature Engineering in Dataflow
    • Writing Transforms
    • ParDo
    • Side Inputs
    • Dataflow SQL
    • Exercise: Feature Engineering with Dataflow  
  • Feature Engineering with BigQuery
    • Tuning Features with SQL
    • Joins
    • Filters 
    • User-Defined Functions
    • Saving Results to Tables
    • Exporting Tables
    • Exercise: Feature Engineering with BigQuery
  • Feature Engineering with Tensorflow
    • Tensorflow Transform
    • Tensorflow Schema
    • Normalizing Features
    • Bucketing Numeric Data
    • Converting Strings to Integer Vocabularies

5. Training and Validating ML Models on Google Cloud

  • BigQuery ML
    • Training ML Models with SQL
    • Model Types
    • Options
    • Splitting the Data
    • Training
    • Validation
    • Prediction
    • Exercise: Programming Models with BigQueryML
  • AutoML
    • Tables
    • Vision
    • Natural Language
    • Video
    • Creating Datasets
    • Labeling
    • Training
    • Validation
    • Exercise: Using AutoML Vision 

6. Vertex AI

  • Vertex AI Basics
    • Vertex AI Workflow
    • Using Vertex AI
  • Managing Training Data
    • Datasets
    • Image Datasets
    • Tabular Datasets
    • Text Datasets
    • Labeling Tasks
  • Managing Models
    • Training
    • Deploying Models
    • Importing Models from BigQuery ML
    • Exporting Models
    • Endpoints
    • Predictions
    • Batch Predictions
    • Exercise: Creating and Deploying Vertex AI Models
  • Vertex AI Pipelines
    • AI Pipeline Architecture
    • TFX vs. Kubeflow Pipelines
    • Google-Provided Pipeline Components
    • Programming Custom AI Pipeline Components
    • Running and Monitoring AI Pipelines
    • Exercise: Running Vertext AI Pipelines

 

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